1. Field of the Invention
The present invention generally relates to a field-based similarity search system and method, and more particularly, a field-based similarity search system and method which identifies similar molecules based on fragment pair feature similarities.
2. Description of the Related Art
An important problem in drug discovery efforts is finding molecules that have a similar function to a molecule known to be active towards a biological target or that elicits a biological response of interest. Commonly, such detailed structural information of the mechanism of action is unknown. Although, there exist several conventional methods to search for or create such compounds, the most common involves a conjectured conformation of the molecule of interest (e.g., query molecule), or the repeated application of a method to a series of query conformers.
More specifically, there exist several conventional methods of aligning a group of flexible molecules to a particular query conformation (e.g., molecular superposition) using computer-assisted drug design (see, e.g., C. Lemmen, T. Lengauer, and G. Klebe, FLEXS: A method for fast flexible ligand superposition. Journal of Medicinal Chemistry, 41:4502-4520, 1998; Michael D. Miller, Robert P. Sheridan, and Simon K. Kearsley, Sq: A program for rapidly producing pharmacophorically relevent molecular superpositions, J. Med. Chem., 42:1505-1514, 1999; Christian Lemmen, Claus Hiller, and Thomas Lengauer, Rigfit: a new approach to superimposing ligand molecules. Journal of Computer-Aided Molecular Design, 11:357 368, 1997; Gerhard Klebe, Thomas Mietzner, and Frank Weber, Different approaches toward an automatic structural alignment of drug molecules: Applications to sterol mimics, thrombin and thermolysin inhibitors, Journal of Computer-Aided Molecular Design, 8:751 778, 1994; Simon K. Kearsley and Graham M. Smith, An alternative method for the alignment of molecular structures: maximizing electrostatic and steric overlap, Journal of Computer Aided Molecular Design, 8:565 582, 1994; Colin McMartin and Regine S. Bohacek, Flexible matching of test ligands to a 3d pharmacophore using a molecular superposition force field: Comparison of predicted and experimental conformations of inhibitors of three enzymes. J. Med. Chem., 42:1505 1514, 1999; S. Handschuh, M. Wagnener, and J. Gasteiger, Superposition of three-dimensional chemical structures allowing for conformational flexibility by a hybrid method, J. Chem. Inf. Comput. Sci., 38:220-232, 1998; J. Mestres. D. C. Rohrer, and G. M. Maggiora, A molecular field-based similarity approach to pharmacophoric pattern recognition, J. Mol. Graph. Modeling, 15:114-21, 1997; Y. C. Martin, M. G. Bures, E. A. Danaher, J. DeLazzer, J. Lico, and P. A. Pavlik, A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists, Journal of Computer Aided Molecular Design, 7:83-102, 1993; Peter Willett, Searching for pharmacophoric patterns in databases of three dimensional chemical structures, Journal of Molecular Recognition, 8:290-303, 1995; Gareth Jones, Peter Willett, and Robin C. Glen, A genetic algorithm for flexible molecular overlay and pharmacophore elucidation, J. Comput.-Aided Mol. Des., 9:532-549, 1995; D. A. Thorner, D. J. Wild, P. Willett, and P. M. Wright, Similarity searching in files of three-dimensional chemical structures: flexible field-based searching of molecular electrostatic potentials, Journal of Chemical Information and Computer Sciences, 36:900 908, 1996; D. J. Wild and P. Willett. Similarity searching in files of 3-dimensional chemical structures—alignment of molecular electrostatic potential fields with a genetic algorithm, Journal of Chemical Information and Computer Sciences, 36:159-167, 1996; David A. Thorner, Peter Willett, Robert C. Glen, P. M. Wright, and Robin Taylor, Similarity searching in files of three-dimensional chemical structures” representation and searching of molecular electrostatic potentials using field-graphs, J. Comput.-Aided Mol. Des., 1:163 174, 1997; and Gerhard Klebe, Structural alignment of molecules, In Hugo Kubinyi, editor, 3D QSAR in Drug Design, pages 173-199. ESCOM, Leiden, 1993).
In the absence of structural information regarding the ligand receptor or ligand-enzyme complex, structural alignment is a way of both elucidating important features responsible for activity (Ki Hwan Kim, List of comfa references 1993-1997, In Hugo Kubinyi, Gerd Folkers, and Yvonne C. Martin, editors, 3D QSAR in Drug Design, volume 3, pages 317 338, Kluwer, Dordrecht/Boston/London, 1998; and Gerhard Klebe, Comparative molecular similarity indicies analysis, In Hugo Kubinyi, Gerd Folkers, and Yvonne C. Martin, editors, 3D QSAR in Drug Design, volume 3, pages 87-104, Kluwer, Dordrecht/Boston/London, 1998) and a means of finding new molecules with similar or better activity (Michael D. Miller, Robert P. Sheridan, and Simon K. Kearsley, Sq: A program for rapidly producing pharmacophorically relevent molecular superpositions, J. Med. Chem., 42:1505-1514, 1999; Andrew C. Good and Jonathan S. Mason, Three dimensional structure database searches, In Kenny B. Lipkowitz and Donald B. Boyd, editors, Reviews in Computational Chemistry, volume 7, chapter 2, pages 67 117. VCH Publishers, Inc., New York, 1996; and Peter Willett, Searching for pharmacophoric patterns in databases of three dimensional chemical structures, Journal of Molecular Recognition, 8:290-303, 1995).
Generally, when one is attempting to elucidate spatial and chemical information about the nature of the host ligand interaction, one often begins with the alignment of a series of active compounds based on some kind of alignment rule. Unfortunately, this process is riddled with difficulties and assumptions about the relevant conformations, relevant features, importance of internal strain, the role of hydrogen bonds, electrostatics, salvation and hydrophobicity, as well as more profound concerns such as whether compounds in a data set even bind at the receptor site via the same mechanism. It is clear that no single method for alignment will settle these issues across widely varying contexts.
Several conventional superposition methods reported are field-based (see, e.g., Michael D. Miller, Robert P. Sheridan, and Simon K. Kearsley, Sq: A program for rapidly producing pharmacophorically relevent molecular superpositions, J. Med. Chem., 42:1505-1514, 1999; Christian Lemmen, Claus Hiller, and Thomas Lengauer, Rigfit: a new approach to superimposing ligand molecules, Journal of Computer-Aided Molecular Design, 11:357 368, 1997; J. Mestres. D. C. Rohrer, and G. M. Maggiora, A molecular field-based similarity approach to pharmacophoric pattern recognition, J. Mol. Graph. Modeling, 15:114-21, 1997; and D. A. Thorner, D. J. Wild, P. Willett, and P. M. Wright, Similarity searching in files of three-dimensional chemical structures: flexible field-based searching of molecular electrostatic potentials, Journal of Chemical Information and Computer Sciences, 36:900 908, 1996). An attractive aspect of field-based approaches is the potential for incorporating high levels of theory into the field. Apart from the difficulties and expense of deploying high level quantum mechanical calculations, the design of a system that can utilize the results of such calculations for use in similarity analysis is considered forward looking.
However, such conventional field-based approaches are confined to a particular field definition. For example, a conventional system designed for simplistic, phenomenological fields, is not available for fields derived from quantum mechanical calculations. This severely limits the versatility of these conventional systems.